From one to many: recent work on truth

In this paper, we offer a brief, critical survey of contemporary work on truth. We begin by reflecting on the distinction between substantivist and deflationary truth theories. We then turn to three new kinds of truth theory—Kevin Scharp's replacement theory, John MacFarlane's relativism, and the alethic pluralism pioneered by Michael Lynch and Crispin Wright. We argue that despite their considerable differences, these theories exhibit a common "pluralizing tendency" with respect to truth. In the final section, we look at the underinvestigated interface between metaphysical and formal truth theories, pointing to several promising questions that arise here

KR3^3: An Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture that combines the complementary strengths of declarative programming and probabilistic graphical models to enable robots to represent, reason with, and learn from, qualitative and quantitative descriptions of uncertainty and knowledge. An action language is used for the low-level (LL) and high-level (HL) system descriptions in the architecture, and the definition of recorded histories in the HL is expanded to allow prioritized defaults. For any given goal, tentative plans created in the HL using default knowledge and commonsense reasoning are implemented in the LL using probabilistic algorithms, with the corresponding observations used to update the HL history. Tight coupling between the two levels enables automatic selection of relevant variables and generation of suitable action policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in large and complex domains. The architecture is evaluated in simulation and on physical robots transporting objects in indoor domains; the benefit on robots is a reduction in task execution time of 39% compared with a purely probabilistic, but still hierarchical, approach.Comment: The paper appears in the Proceedings of the 15th International Workshop on Non-Monotonic Reasoning (NMR 2014

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Housing markets and independence in old age: expanding the opportunities

This report highlights the benefits of specialised private retirement accommodation and recommends a number of simple policy changes at no cost to the public purse to help increase its supply and address the challenges of housing an ageing population

Analysis of terrestrial and Martian volcanic compositions using thermal emission spectroscopy

This dissertation comprises four separate parts, revised from individual research papers, which address the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) investigation objective of determining and mapping the composition and distribution of surface minerals and rocks on Mars from orbit. Each part is self-contained and addresses a specific aspect of this objective while collectively building on results of the previous studies. In Part 1, laboratory thermal infrared spectra (5-25 μm, at 2 cm-1 spectral sampling), deconvolved modal mineralogies, and derived mineral and bulk rock chemistries of basalt, basaltic andesite, andesite, and dacite were used to evaluate and revise volcanic rock classification schemes. Modal mineralogies derived from linear deconvolution of terrestrial volcanic rocks were compared to modes measured by an electron microprobe phase-mapping technique to determine the accuracy of linear deconvolution in modeling specific mineral abundances. One-cr standard deviations of the absolute differences between modeled and measured mineral abundances range from 2.4 to 12.2 vol %, with an average standard deviation of 4.8 vol % being in agreement with average uncertainties calculated in previous studies. Weighted average compositions of feldspars in the deconvolution generally overlap the measured ranges of plagioclase compositions and the presence of low-calcium and high-calcium pyroxenes was correctly identified